Design of a PD‐type learning observer for reconstruction of actuator faults in descriptor systems.

This study investigates the problem of reconstructing actuator faults for descriptor systems via a PD‐type learning observer. By synthesising the derivatives of the output estimation error into the P‐type learning law, a novel PD‐type learning observer is established to simultaneously reconstruct original system states and actuator faults. Stability analysis of the PD‐type learning observer is explicitly provided. A systematic design method is also suggested based on a linear matrix inequality technique. Further, a robust PD‐type learning observer is designed against process disturbances and measurement noises. At last, a simulation example is used to demonstrate the effectiveness of the proposed fault‐reconstructing method. [ABSTRACT FROM AUTHOR]